class_docstr: Res-Net network structure
class_docstr_tok: ["Res-Net","network","structure"]
docstr: Args:
      block (block): convolution block
      planes (int): input channels
      blocks (list): layers of the block
      stride (int): stride of the convolution
  
  Returns:
      nn.Sequential: the combination of the convolution block
docstr_tok: ["Args",":","block","(","block",")",":","convolution","block","planes","(","int",")",":","input","channels","blocks","(","list",")",":","layers","of","the","block","stride","(","int",")",":","stride","of","the","convolution","Returns",":","nn.Sequential",":","the","combination","of","the","convolution","block"]
code: def _make_layer(self, block, planes, blocks, stride=1):
      """
  
      Args:
          block (block): convolution block
          planes (int): input channels
          blocks (list): layers of the block
          stride (int): stride of the convolution
  
      Returns:
          nn.Sequential: the combination of the convolution block
  
      """
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2D(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  weight_attr=conv_weight_attr,
                  bias_attr=False,
              ),
              nn.BatchNorm2D(planes * block.expansion),
          )
  
      layers = list()
      layers.append(block(self.inplanes, planes, stride, downsample))
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(block(self.inplanes, planes))
  
      return nn.Sequential(*layers)
  
file_path: PaddleOCR/ppocr/modeling/backbones/rec_resnet_32.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle.nn
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride) and returns: nn